The long-term objective of this research is the development of a computational model of perceptual categorization and memory, which interrelates performance across a variety of tasks, including classification, identification, old-new recognition, and same- different judgment. The present project is organized around the continued development and testing of Nosofsky and Palmeri's exemplar-based random walk (EBRW) model. According to the EBRW, people represent categories by storing individual exemplars in memory. Test objects cause individual exemplars to be retrieved based on how similar the objects are to the exemplars. The retrieved exemplars provide evidence that enters into a random-walk process for making classification decisions. The EBRW goes beyond previous work by providing a detailed processing account of the time course of categorization decision making, thereby allowing the model to jointly predict classification choice probabilities and response times. One goal of the new work is to extend the EBRW with a stochastic dimensional encoding process to allow it to predict response times for separable-dimension stimuli as well as integral- dimension ones. A second goal is to extend the model to the domain of multidimensional same-different judgment. Finally, the project will investigate the extent to which the exemplar-based model can account for a wide variety of empirical phenomena which previous investigators have recently interpreted in terms of rule abstraction or prototype formation. Understanding the fundamental processes of perceptual categorization and recognition is one of the central goals of research in memory and cognition. A direct health-related application of the present work would be to provide information about how radiologists make disease classifications on the basis of imperfect information contained in X-ray displays, with the ultimate goal of developing training techniques as well as computer technology to assist in radiological decision making.